What drives volatility of the US oil and gas firms?

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LYÓCSA Štefan TODOROVA Neda

Rok publikování 2021
Druh Článek v odborném periodiku
Časopis / Zdroj Energy Economics
Fakulta / Pracoviště MU

Ekonomicko-správní fakulta

Citace
www https://www.sciencedirect.com/science/article/pii/S014098832100270X
Doi http://dx.doi.org/10.1016/j.eneco.2021.105367
Klíčová slova Oil & Gas sub-industry; Volatility forecasting; Volatility factors; HAR; Dynamic Model Averaging
Přiložené soubory
Popis We study how the day-ahead stock price volatility of 15 firms that are S&P 500 constituents in the Oil & Gas Exploration & Production sub-industry is driven through six volatility factors represented by realized volatilities, namely, (i) firms’ own volatility, (ii) industry market volatility, (iii) local (U.S.) market volatility, (iv) world equity market volatility, (v) oil price volatility, and (vi) natural gas price volatility. Existing studies have reported results based on analysis of one or few volatility components, but given the high dependence among volatility factors, this might bias (overestimate) the true importance of each of the volatility factors on the price fluctuation of stocks in the Oil & Gas Exploration & Production sub-industry. To take into account this inter-relatedness of volatility factors, we study all volatility factors together. Using augmented heterogeneous autoregressive (HAR) models and dynamic model averaging, our analysis shows that market volatility is most influential, followed by a stock’s own volatility and industry level volatility. The role of the volatility of the oil market is of lesser importance, while the volatility of the world equity market does not appear to contain incremental information useful for predicting the volatility of firms in the Oil & Gas Exploration & Production sub-industry. The role of the natural gas market is specific. An in-sample analysis suggests a negative relationship between firm-level volatility and volatility on the natural gas market. However, in an out-of-sample framework, the volatility of the natural gas market appears to be unrelated to firm-level volatility. Dynamic model averaging further suggests that the market and industry factors are time-varying. These findings have implications for financial risk management, as we show that in an out-of-sample framework, HAR models augmented with volatility factors outperform the plain HAR model by up to a 3.88% increase in volatility forecast accuracy.
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